Most Few-shot font generation (FFG) methods focus on disentangling the content feature and style feature from the given glyphs. Based on different kinds of feature representation, FFG methods can be divided into two main categories: global feature representation [1]}, [2]}, [3]}, [4]} and component-based feature representation [5]}, [6]}, [7]}, [8]}. In methods that apply global feature representation, vectors related to content and style are extracted from content glyphs and reference glyphs respectively. MCGAN [1]} synthesize the ornamented glyphs with stacked conditional GANs to extract features from input images. EMD[2]} and AGIS-Net[3]} combine a style vector and content vector together to synthesize a glyph. ZiGAN[4]} matches the features to Hilbert space to better capture the structural information. Works related to component-based feature representation concentrate on devising a feature representation that is related to the glyphs' components or localized features. RD-GAN [5]} uses a radical encoder to extract features of glyphs' specific components. In DM-Font [6]}, it disassembles glyphs to stylized components and reassembles them to new glyphs by utilizing strong compositionality prior. LF-Font [7]} designs a component-conditioned reference encoder to extract component-wise features from reference images. MX-Font [8]} employs multiple encoders for each reference image with disentanglement between content and style which makes the cross-lingual task possible. DG-Font [17]} is an unsupervised framework based on TUNIT [18]} by replacing the traditional convolutional blocks with Deformable blocks which enables the model to perform better on cursive characters which are more difficult to generate.
